Evaluating a Structured Reporting Template to Increase Transparency and Reduce Review Time for Healthcare Database Studies

Evaluating a structured reporting template to increase transparency and reduce review time for healthcare database studies

 

Importance of transparency for RWE from databases

Quality and rigor of database studies are variable…

• ●Broad dismissal of database studies as inferior, less valid 

Lack of transparency is an important barrier to use of

‘real world’ evidence from databases for decision making

• ●Without transparency, unable to assess validity/relevance 

 

Steps to increase transparency about how RWE is generated

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The International Society for Pharmacoeconomics and Outcomes Research (ISPOR)/

International Society for Pharmacoepidemiology (ISPE)

Joint Task Force on Real World Evidence for Healthcare Decision-Making

Shirley starts

 

Steps to increase transparency about how RWE is generated

<number>

The International Society for Pharmacoeconomics and Outcomes Research (ISPOR)/

International Society for Pharmacoepidemiology (ISPE)

Joint Task Force on Real World Evidence for Healthcare Decision-Making

Shirley starts

 

Specific reporting to improve transparency and reproducibility and facilitate validity assessment

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DATA SOURCE

• ●Data provider 

• ●Data extraction date (DED)* 

• ●Data sampling 

• ●Source data range (SDR)* 

• ●Type of data 

• ●Data linkage, other supplemental data 

• ●Data cleaning 

• ●Data model conversion 

DESIGN

• ●Design diagram 

INCLUSION/EXCLUSION CRITERIA

• ●Study entry date (SED)* 

• ●Person or episode level study entry 

• ●Sequencing of exclusions 

• ●Enrollment window (EW)* 

• ●Enrollment gap 

• ●Inclusion/Exclusion definition window 

• ●Codes 

• ●Frequency and temporality of codes 

• ●Diagnosis position (if relevant/available) 

• ●Care setting 

• ●Washout for exposure 

• ●Washout for outcome 

CONTROL SAMPLING

• ●Sampling strategy 

• ●Matching factors 

• ●Matching ratio 

EXPOSURE DEFINITION

• ●Type of exposure 

• ●Exposure risk window (WRW) 

• ●Induction period 

• ●Stockpiling 

• ●Bridging exposure episodes 

• ●Exposure extension 

• ●Switching/add on z 

• ●Codes, frequency and temporality of codes, diagnosis position, care setting 

• ●Exposure Assessment Window (EAW)* 

FOLLOW UP TIME

• ●Follow-up window (FW)* 

• ●Censoring criteria 

OUTCOME DEFINITION

• ●Event date (ED)* 

• ●Validation 

• ●Codes, frequency, and temporality of codes, diagnosis position, care setting 

COVARIATE DEFINITIONS

• ●Covariate assessment window (CW)* 

• ●Comorbidity/risk score 

• ●Healthcare utilization metrics 

• ●Codes, frequency, and temporality of codes, diagnosis position, care setting 

STATISTICAL SOFTWARE

• ●Statistical software program used 

* key temporal anchors

 

<number>

DATA SOURCE

• ●Data provider 

• ●Data extraction date (DED)* 

• ●Data sampling 

• ●Source data range (SDR)* 

• ●Type of data 

• ●Data linkage, other supplemental data 

• ●Data cleaning 

• ●Data model conversion 

DESIGN

• ●Design diagram 

INCLUSION/EXCLUSION

CRITERIA

• ●Study entry date (SED)* 

• ●Person or episode level study entry 

• ●Sequencing of exclusions 

• ●Enrollment window (EW)* 

• ●Enrollment gap 

• ●Inclusion/Exclusion definition window 

• ●Codes 

• ●Frequency and temporality of codes 

• ●Diagnosis position (if relevant/available) 

• ●Care setting 

• ●Washout for exposure 

• ●Washout for outcome 

CONTROL SAMPLING

• ●Sampling strategy 

• ●Matching factors 

• ●Matching ratio 

EXPOSURE DEFINITION

• ●Type of exposure 

• ●Exposure risk window (WRW) 

• ●Induction period 

• ●Stockpiling 

• ●Bridging exposure episodes 

• ●Exposure extension 

• ●Switching/add on z 

• ●Codes, frequency and temporality of codes, diagnosis position, care setting 

• ●Exposure Assessment Window (EAW)* 

FOLLOW UP TIME

• ●Follow-up window (FW)* 

• ●Censoring criteria 

OUTCOME DEFINITION

• ●Event date (ED)* 

• ●Validation 

• ●Codes, frequency and temporality of codes, diagnosis position, care setting 

COVARIATE DEFINITIONS

• ●Covariate assessment window (CW)* 

• ●Comorbidity/risk score 

• ●Healthcare utilization metrics 

• ●Codes, frequency, and temporality of codes, diagnosis position, care setting 

STATISTICAL SOFTWARE

• ●Statistical software program used 

* key temporal anchors

 

Interim results from large scale replication of peer-reviewed database studies

Calibration of effect estimates¹ for publication versus direct replication

Glass half full	Glass half empty
•74% of effect estimates on same side of null	•26% of effect estimates on opposite side of null
•88% of CI had any overlap²	•12% of CI had no overlap²
•If CI overlapped, 40% of CI range was shared³	•If CI overlapped, 60% of CI range was not shared³

 

Go to structured template

 

Example from JAMA Internal Medicine

Abstract:

We identified participants as those newly diagnosed as having atrial fibrillation (AF) from October 1, 2010, through October 31, 2011, and who initiated dabigatran or warfarin treatment within 60 days of initial diagnosis.

Methods:

We identified patients who were newly diagnosed as having AF from October 1, 2010, through October 31, 2011, by using the CMS Chronic Condition Warehouse indicator that traced the first diagnosis date back to January 1, 1999. The diagnosis of AF was defined as having 1 inpatient or 2 outpatient claims with primary or secondary International Classification of Diseases, Ninth Revision (ICD-9), code 427.31. We also required that individuals in our study sample had filled an outpatient prescription for either dabigatran or warfarin within 2 months of the first diagnosis (N = 9562). Those who filled prescriptions for dabigatran and warfarin during the first 2 months after diagnosis were excluded (N = 158). We followed up each individual from the first prescription of dabigatran or warfarin until discontinuation of use for more than 60 days, switch of anticoagulants, death, or December 31, 2011. Our final overall study sample included 1,302 dabigatran users and 8,102 warfarin users.

 

 

 

 

 

 

 

 

 

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